Abstract: Brain tumor MRI images usually exhibit large variations in tumor scale, strong morphological heterogeneity, and blurred lesion boundaries that are often intertwined with normal brain tissues. Existing detection models still suffer from insufficient local detail extraction, global semantic modeling, and cross-scale feature fusion, which may lead to inaccurate boundary localization, increased missed and false detections, and high deployment costs. To address these issues, this paper proposes GDC-DETR, a high-accuracy and lightweight brain tumor detection model based on RTDETR-r18. The proposed model aims to improve the detection accuracy of complex lesions while reducing the number of parameters and computational cost, making it more suitable for clinical computer-aided diagnosis and lightweight deployment. GDC-DETR is improved from three aspects: backbone enhancement, feature fusion, and efficient feature interaction. First, a gated enhancement module with contextual awareness, named GLCA-CG, is designed. It extracts edge and texture details and long-range contextual information through local and global dual branches, respectively, and adaptively selects discriminative features using a convolutional gating mechanism. This enhances the model’s ability to distinguish small lesions, weak boundaries, and complex backgrounds. Second, a hierarchical local-global attention fusion module, named HLGA-FM, is constructed. By employing parallel patch-aware attention at local and global scales, this module achieves semantic alignment and adaptive fusion between high-level and low-level features, alleviating information dilution, channel redundancy, and semantic deviation caused by traditional feature concatenation, thereby improving cross-scale lesion localization. Finally, a low-resolution lightweight attention-based intra-scale feature interaction module, named LR-AIFI, is proposed. High-resolution features are first mapped into a low-resolution space through anti-aliasing downsampling for attention interaction, and then spatial details are compensated through upsampling and depthwise convolution refinement. As a result, the computational complexity of the main attention term is reduced to approximately 1/16 of the original strategy, achieving a better balance between global modeling capability and computational efficiency. Experiments are conducted on a public brain tumor MRI dataset containing three types of tumors: glioma, meningioma, and pituitary tumor. Under unified training parameters, evaluation metrics, and experimental environments, GDC-DETR is compared with RTDETR series models, YOLO series models, and several brain tumor detection methods. The experimental results show that GDC-DETR achieves an mAP50 of 92.9%, which is 2.7 percentage points higher than the RTDETR-r18 baseline. The precision and recall reach 91.4% and 91.1%, respectively. Meanwhile, the number of parameters is reduced to 15.6 M, representing a 21.6% decrease compared with the baseline, and the computational cost is 47.8 GFLOPs, maintaining a relatively low computational overhead among the mainstream comparison models. Visualization results indicate that, for challenging samples with blurred boundaries, irregular shapes, and high interference between lesions and surrounding tissues, GDC-DETR can more accurately focus on the tumor core region, reduce invalid responses in background areas, and obtain more stable bounding-box localization results. Ablation experiments demonstrate the effectiveness of each proposed module. Introducing GLCA-CG alone improves mAP50 by 1.2 percentage points and significantly reduces the parameter scale. Combining HLGA-FM with GLCA-CG further improves mAP50 to 92.4%. LR-AIFI reduces the computational burden while maintaining stable detection accuracy. The best overall performance is achieved when the three modules are jointly integrated, indicating that detail enhancement, multi-scale semantic fusion, and low-resolution efficient interaction are complementary to each other. Generalization experiments further show that the proposed model achieves mAP50 values of 94.9% and 92.9% on two external MRI datasets, respectively. In the zero-annotation transfer scenario, the mAP50 reaches 73.5%, and after fine-tuning with only 10% and 20% of target-domain annotations, it increases to 86.5% and 88.6%, respectively, demonstrating favorable cross-domain adaptability and few-shot transfer capability. In summary, GDC-DETR effectively alleviates the trade-off between high accuracy and low computational cost in brain tumor MRI detection, improves the sensitivity and localization ability for complex lesions, and reduces the dependence on hardware resources, providing a lightweight reference solution for clinical auxiliary brain tumor detection.

摘要： 摘 要: 针对脑肿瘤磁共振成像（MRI）影像中肿瘤尺度差异大、形态异质性强，病灶边界常与正常脑组织交融，现有检测模型在局部细节提取、全局语义建模和跨尺度融合方面仍存在不足，易造成边界定位偏差、漏检误检增加及部署成本较高。针对上述问题，本文以基于ResNet-18骨干网络的实时检测Transformer模型（RTDETR-r18）为基线，提出高精度轻量化脑肿瘤检测模型（GDC-DETR），旨在提升复杂病灶检测精度的同时降低模型参数量和计算量，使模型更适合临床辅助诊断和轻量化部署需求。模型从主干增强、特征融合和高效交互三个层面改进。首先，设计融合上下文感知的门控增强模块（GLCA-CG），在局部与全局双分支中分别提取边缘纹理细节和长距离上下文信息，并通过卷积门控机制自适应筛选关键特征，增强对小病灶、弱边界和复杂背景的判别能力。其次，构建层次化局部-全局注意力融合模块（HLGA-FM），通过局部尺度与全局尺度的并行感知注意力（Patch）实现高、低层特征的语义对齐和自适应融合，缓解传统特征拼接造成的信息稀释、通道冗余和语义偏差问题，提高跨尺度病灶定位能力。最后，提出低分辨率轻量特征交互模块（LR-AIFI），先采用抗混叠下采样将高分辨率特征映射至低分辨率空间进行注意力交互，再通过上采样与深度卷积细化补偿空间细节，使注意力主项计算复杂度约降至原策略的1/16，从而兼顾全局建模能力与计算效率。 本文在包含神经胶质瘤、脑膜瘤和垂体瘤三类脑肿瘤的公开MRI数据集上验证，并采用统一训练参数、统一评价指标和相同实验环境与RTDETR系列、YOLO系列及多种脑肿瘤检测模型进行对比。实验结果表明，GDC-DETR的平均精度均值(mAP50)达到92.9%，较RTDETR-r18基线提升2.7个百分点，精确率和召回率分别为91.4%和91.1%；参数量为15.6 M，较基线减少21.6%，计算量为47.8十亿次浮点运算量(GFLOPs)，在参与对比的主流模型中保持较低计算开销。可视化结果表明，在边界模糊、形态不规则及病灶与周围组织高度混杂的困难样本中，GDC-DETR能够更准确地聚焦肿瘤核心区域，减少背景区域无效响应，并获得更稳定的检测框定位结果。消融实验显示，GLCA-CG单独引入后mAP50提升1.2个百分点，并显著压缩参数规模；HLGA-FM与GLCA-CG联合后mAP50提升至92.4%；LR-AIFI在降低计算负担的同时保持稳定精度。三种模块联合后取得最优综合性能，说明细节增强、多尺度语义融合和低分辨率高效交互具有互补作用。泛化实验表明，模型在两类外部MRI数据集上的mAP50分别达到94.9%和92.9%；在零标注迁移场景下mAP50为73.5%，仅使用10%和20%目标域标注微调后分别提升至86.5%和88.6%，表现出较好的跨域适应性和小样本迁移能力。综上，GDC-DETR能够缓解脑肿瘤MRI检测中高精度与低计算成本难以兼顾的问题，提高复杂病灶检测灵敏度和定位能力，并降低模型部署对硬件资源的依赖，可为临床脑肿瘤辅助检测提供轻量化参考方案。